March 16, 2020 Journal article Open Access

Stefanos Georganos; Tais Grippa

JSON-LD (schema.org) Export

{
  "description": "<p>This is a very-high-resolution map of Kampala derived from satellite imagery of Pleiades (0.5m) collected in February 2013.</p>\n\n<p>The pixel values related to the following legend:</p>\n\n<p>2: Water</p>\n\n<p>3: Tree vegetation</p>\n\n<p>4:Low vegetation</p>\n\n<p>5:Bare ground</p>\n\n<p>6: Artificial ground surface</p>\n\n<p>7:Buildin</p>\n\n<p>8:Shadow</p>\n\n<p>The Out of Bag error of the product is 14,14%. The class errors are:</p>\n\n<p>Water =&nbsp;0.077748</p>\n\n<p>Tall Vegetation =&nbsp;0.410714</p>\n\n<p>Low Vegetation =&nbsp;0.087757</p>\n\n<p>Bare Ground =&nbsp;0.336689</p>\n\n<p>Artificial Ground Surface =&nbsp;0.197932</p>\n\n<p>Building =&nbsp;0.058271</p>\n\n<p>Shadow =&nbsp;0.062032</p>\n\n<p>References:</p>\n\n<p>[1]&nbsp;Grippa, Ta&iuml;s, Moritz Lennert, Benjamin Beaumont, Sabine Vanhuysse, Nathalie Stephenne, and El&eacute;onore Wolff. 2017. &ldquo;An Open-Source Semi-Automated Processing Chain for Urban Object-Based Classification.&rdquo;&nbsp;<em>Remote Sensing</em>&nbsp;9 (4): 358.&nbsp;<a href=\"https://doi.org/10.3390/rs9040358\">https://doi.org/10.3390/rs9040358</a>.</p>\n\n<p>[2]&nbsp;Grippa, Tais, Stefanos Georganos, Sabine G. Vanhuysse, Moritz Lennert, and El&eacute;onore Wolff. 2017. &ldquo;A Local Segmentation Parameter Optimization Approach for Mapping Heterogeneous Urban Environments Using VHR Imagery.&rdquo; In&nbsp;<em>Proceedings Volume 10431, Remote Sensing Technologies and Applications in Urban Environments II.</em>, edited by Wieke Heldens, Nektarios Chrysoulakis, Thilo Erbertseder, and Ying Zhang, 20. SPIE.&nbsp;<a href=\"https://doi.org/10.1117/12.2278422\">https://doi.org/10.1117/12.2278422</a>.</p>\n\n<p>[3]&nbsp;Georganos, Stefanos, Ta&iuml;s Grippa, Moritz Lennert, Sabine Vanhuysse, and Eleonore Wolff. 2017. &ldquo;SPUSPO: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Efficiently Segmenting Large Heterogeneous Areas.&rdquo; In&nbsp;<em>Proceedings of the 2017 Conference on Big Data from Space (BiDS&rsquo;17)</em>.</p>\n\n<p>This research was funded by BELSPO (Belgian Federal Science Policy Office) in the frame of the STEREO III program, as part of the REACT (SR/00/337) project (<a href=\"http://react.ulb.be/\">http://react.ulb.be/</a>).</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Universit\u00e9 Libre de Bruxelles", 
      "@type": "Person", 
      "name": "Stefanos Georganos"
    }, 
    {
      "affiliation": "Universit\u00e9 Libre de Bruxelles", 
      "@type": "Person", 
      "name": "Tais Grippa"
    }
  ], 
  "headline": "Kampala Very-High-Resolution Land Cover Map", 
  "image": "https://zenodo.org/static/img/logos/zenodo-gradient-round.svg", 
  "datePublished": "2020-03-16", 
  "url": "https://zenodo.org/record/3711905", 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3711905", 
  "@id": "https://doi.org/10.5281/zenodo.3711905", 
  "@type": "ScholarlyArticle", 
  "name": "Kampala Very-High-Resolution Land Cover Map"
}